Performance terms

67 terms of 1546 total · page 1 of 4

🚀 Every millisecond matters — here is how to win them back

Slow software costs money, loses users, and erodes trust. Performance engineering covers profiling, caching strategies, database query optimisation, network latency, memory management, and the architectural patterns that keep systems fast under real-world load. Most performance problems are invisible until they are not — these terms help you find and fix them before your users do.

More on Performance history · 5 references · key concepts

History

Performance optimization emerged as a distinct discipline in the 1960s–70s as computing resources were scarce and expensive; early work focused on algorithmic efficiency (Big O notation) and compiler optimization. The rise of web applications in the 1990s–2000s shifted focus to network latency, database query efficiency, and caching strategies, driven by real user experience demands. The 2010s saw standardization efforts like Google's Core Web Vitals and widespread adoption of profiling tools, CDNs, and in-memory caches (Redis, Memcached), making performance measurable and addressable at scale. Modern practice integrates continuous monitoring, synthetic benchmarking, and observability platforms to track tail latency and resource consumption across distributed systems. Today, performance is embedded into development workflows as a non-functional requirement, with frameworks and tooling built to optimize by default—from PHP JIT compilation to HTTP/3 and edge computing.

Key concepts

Time Complexity (Big O)
Space Complexity
Database Indexing
N+1 Query Problem
Caching Strategies
HTTP Caching (ETags
Cache-Control)
Core Web Vitals
Profiling & Benchmarking

Best references

Web Vitals Google's official guidance on Core Web Vitals (LCP, FID, CLS) and their impact on search ranking. Essential reading for understanding modern performance metrics.
High Performance Browser Networking Ilya Grigorik's comprehensive book covering TCP, TLS, HTTP/1.1, HTTP/2, HTTP/3, and optimization strategies. Authoritative reference for networking fundamentals underlying web performance.
Redis Documentation Official Redis docs covering data structures, persistence, clustering, and performance patterns. Canonical source for Redis caching and pub/sub strategies.
PostgreSQL Documentation: Indexes & Query Planning Authoritative reference for database indexing, EXPLAIN query analysis, and index selectivity. Directly applicable to query optimization and covering indexes.
The Art of Computer Systems Performance Analysis Brendan Gregg's performance analysis methodology and tools reference. Foundation for systematic profiling, tail latency analysis, and flame graphs.

🧠 Logic graph · Performance

61 edges here

17 verbs used

69 terms touched

3 narratives

✦ Latest in Performance

Typed relationships here

Edges touching a Performance term.

Redis Often seen in Async Processing / Job Queues 3d
Cache Stampede / Thundering Herd Often seen in Redis 4d
Redis Alternative to APCu — In-Process User Cache 4d
Load Testing Precedes Profiling & Benchmarking 4d
Database Indexing Leverages Binary Trees Aug 22

Rate limiting caps request volume at the edge, but on its own it can still let one noisy tenant drain shared pools before the limiter kicks in. The bulkhead pattern complements it by partitioning resources per client or workload, and in doing so it applies the defence-in-depth principle: if the rate limiter is misconfigured or bypassed, the bulkhead is an independent layer that still contains the blast radius. So the bulkhead is what turns a single throttling control into a layered defence.

Rate Limiting · via · 🪢 Bulkhead Pattern → Defence in Depth

Garbage collection is the concrete piece of memory management that touches memory leaks directly: as a specialization of the heap-management side of the discipline, it's the runtime mechanism responsible for reclaiming unreachable objects. A leak is precisely what happens when that reclamation fails — most acutely in PHP when reference counting can't free circular references and the cycle collector is what stands between you and unbounded process growth. So the story from memory_management to memory_leak runs through GC because GC is the part of memory management whose success or failure determines whether allocations are ever returned.

Memory Management — Stack vs Heap · through · 🪢 Garbage Collection → Memory Leak

Binary trees connect to pagination through database indexing, which is the structure that actually carries the benefit between them. The index is typically a B-tree variant of the binary tree concept, and its ordered, logarithmic-lookup structure is exactly what lets efficient pagination seek directly to a page's starting key (keyset pagination) instead of scanning and counting rows. Without the index sitting in the middle, the tree's ordering properties would never reach pagination at all — it's the index that translates tree structure into fast page boundaries.

Binary Trees · by means of · 🪢 Database Indexing → Pagination Strategies

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Most referenced — Performance

Redis 1 Memory Pressure Detection 1

RoadRunner — Persistent PHP Worker 3 Caching Strategies 2 Eager vs Lazy Loading — When to Use Each 2 APCu — In-Process User Cache 2 Swoole / OpenSwoole 2 Async Processing / Job Queues 1 Pagination Strategies 1 Lazy Loading 1

RoadRunner — Persistent PHP Worker 256APCu — In-Process User Cache 220Database Indexing 130Profiling & Benchmarking 121Async Processing / Job Queues 109Caching Strategies 107Query Optimisation 103N+1 Query Problem 103

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Category total5.6k pings Terms pinged67 / 67 Distinct agents24

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